Radar imaging techniques, such as inverse synthetic aperture radar (ISAR), rely on measuring the Doppler shifts induced by relative motion between the target and the radar to generate an image of the target. High range resolution is achieved using pulse compression techniques, whereas high cross-range resolution relies on the accurate measurement of the Doppler shifts induced by uniform rotational motion of the target. In traditional ISAR processing, numerous pulses over a period of time must be processed using Fourier processing to measure the Doppler frequency of the moving target. If target scatterers move out of their range cells during the imaging time, or if the rotational motion is not uniform, the image will be smeared. Therefore, motion compensation algorithms must be used to produce a focused ISAR image.
Pulse compression allows a radar to obtain the range resolution of a short pulse without the need for very high peak transmit power by transmitting a long pulse that is phase or frequency modulated. The modulated pulse or waveform, is reflected back to the radar by scatterers that lie in the transmission path. This process can be viewed as the convolution of the transmitted waveform with an impulse response that is representative of the range profile illuminated by the radar. The purpose of pulse compression is then to estimate the range profile impulse response based upon the known transmitted waveform and the received radar return signal. The traditional method of pulse compression, known as matched filtering, has been shown to maximize the received signal-to-noise ratio (SNR), of the target return. A matched filter is applied by convolving the received signal with the time-reversed complex conjugate of the transmitted waveform. The traditional matched filter is limited by the range sidelobes produced by the filtering process. The sidelobes of large targets can mask the presence of nearby small targets, thus limiting the sensitivity of the radar.
Adaptive pulse compression (APC) by way of Reiterative Minimum Mean-Square Error (RMMSE), described in U.S. Pat. No. 6,940,450, issued Sep. 6, 2005 and incorporated herein by reference, is capable of accurately estimating the range profile illuminated by a radar by suppressing range sidelobes to the level of the noise floor. This is accomplished by adaptively estimating the appropriate receiver pulse compression filter to use for each individual range cell. Furthermore, the RMMSE algorithm, which has also been denoted as Adaptive Pulse Compression (APC) when applied to the radar pulse compression problem, has been shown to be robust to rather severe Doppler mismatch. A multistatic adaptive pulse compression (MAPC) formulation that can resolve a radar target in the presence of multiple radar return signals occupying a shared frequency spectrum is described in U.S. Ser. No. 11/268,755, filed Nov. 7, 2005 now U.S. Pat. No. 7,474,257, incorporated herein by reference (hereinafter “MAPC”).
It would be desirable to provide an adaptive radar processing system that can resolve a moving radar target image from a single transmitted pulse using a Doppler-sensitive variation of the multistatic adaptive pulse compression formulation thereby mitigating the need for motion compensation.